The paper is about Analysis of feature extraction and channel compensation in GMM speaker recognition system.

Abstract

In this paper, several feature extraction and channel compensation techniques found in state-of-the-art speaker verification systems are analyzed and discussed. For NIST SRE 2006 submission, Cepstral Mean Subtraction, Feature Warping, RASTA filtering, HLDA, Feature Mapping and eigenchannel adaptation were incrementally added to minimize system's error rate. The key-part of the paper is however the post-evaluation analysis, undermining the common myth "the more boxes in the scheme, the better system". All results are presented on NIST SRE 2005 and 2006 data.